{
 "cells": [
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 2,
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter Notebook使用范例"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 1,
   "source": [
    "导入pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot使用范例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylab import *\n",
    "from matplotlib.pylab import figure,plot,xlabel,ylabel,title,show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3X/8zfX9//HbI1FIysiHImtf6/eo3qFZTajUFP1QKamWsT5TWbV+rF82bSmr9WvVR4iQkhAyJWMxRRSborVZSuRHKD/y4+39+P7xPNY74X3wfp3n+XG/Xi7vyznv8zrvvR6n2ut+nj9ez6e5OyIiUrj2iV2AiIjEpSAQESlwCgIRkQKnIBARKXAKAhGRAqcgEBEpcAoCKQhmdpWZTcuCOt4zsxa7OD7FzLpksCQRBYHInjCzFma2uIz3DDSze0u/5u7HuvuU1PGeZjYkwTJF0qIgENlNZrZv7BpEypOCQPKKmdUzs5FmtsLMPjezx7c7/gczW21m/zGzs0u9frWZzTeztWa20My6lTrWwswWm9mtZvYZMAz4M1DXzNalfupud56uwOXALanjY1Ovf2Rmrc2sDfBr4JLU8bk7+Tw/TdW12sxeNbPDy+kflch/KQgkb5hZBWAcsAhoABwKPF/qLU2BD4CawANAfzOz1LHlQFvgQOBq4I9mdmKpv/0foAZwONAZOBtY4u4HpH6WlK7F3fsCQ4EHUsfP3e74BOD3wAup44128HnaE8LiAqAWMJUQQiLlSkEg+aQJUBf4lbuvd/eN7l56gHiRuz/t7luBQUAdoDaAu7/i7v/24K/Aa8Cppf62BLjH3Te5+1eZ+Th0A+5z9/nuXkwIjsZqFUh5UxBIPqlHuNgX7+T4Z9ueuPuG1NMDAMzsbDN7y8xWmdka4BxCy2GbFe6+MYmid+Fw4BEzW5OqaRVghJaOSLlREEg++QSov7uDuWa2H/AS8AegtrsfBIwnXHS32X6Z3nSW7S3rPWUd/wTo5u4Hlfqp7O7T0zi3SNoUBJJPZgJLgd5mVtXM9jez5mn8XSVgP2AFUJwaRD6zjL9ZBnzHzKqX8Z4jyjjewMx29v/Dp4DbzexYADOrbmYdyqhLZLcpCCRvpPr+zwX+H/AxsBi4JI2/WwtcDwwHVgOXAWPK+JsFhIHbhamum7o7eFt/4JjU8dE7OP5i6vFzM3tnB+cYBdwPPG9mXwLzCIPUIuXKtDGNiEhhU4tARKTAKQhERAqcgkBEpMApCEREClxOLJ5Vs2ZNb9CgQewyRERyyuzZs1e6e62y3pcTQdCgQQNmzZoVuwwRkZxiZovSeZ+6hkRECpyCQESkwCkIREQKnIJARKTAKQhERApcYkGQ2jJwcmqbvffM7IbU6z3N7FMzm5P6OSepGkREpGxJTh8tBm5y93fMrBow28wmpo790d3/kOC5RUQkTYm1CNx9qbu/k3q+FpiPdlYSEUnPV1/B9dfD558nfqqMjBGYWQPgBGBG6qXuZvZ3MxtgZgfv5G+6mtksM5u1YsWKTJQpIpId3OGaa+Dxx+HttxM/XeJBYGYHELYB7OHuXwJPAt8DGhN2k3pwR3/n7n3dvcjdi2rVKvMOaRGR/NGnDwwbBr/7HbRpk/jpEg0CM6tICIGh7j4SwN2XuftWdy8BngaaJFmDiEhOGT8ebrsNLrkkPGZAkrOGjLBV33x3f6jU63VKve18wvZ7IiKyYAF07AiNG8OAAWCWkdMmOWuoOXAF8A8zm5N67ddARzNrDDjwEdAtwRpERHLDmjXQrh3stx+MHg1VqmTs1IkFgbtPA3YUZ+OTOqeISE7aujW0BP7zH5g0CerXz+jpc2IZahGRvHb77TBhAvzf/8Gpp2b89FpiQkQkpqFDwyyh//1f6No1SgkKAhGRWGbNgi5d4Mc/hocfjlaGgkBEJIalS6F9e6hdG158ESpWjFaKxghERDJt0ya48EJYvRqmT4fIN80qCEREMsk9jAe8+WZoCTRqFLsidQ2JiGTUY4+Fm8Xuugsuuih2NYCCQEQkcyZNghtvDDeO9ewZu5r/UhCIiGTCv/8NHTrAUUfB4MGwT/ZcfrOnEhGRfLV2bWgFmMHLL0O1arEr+gYNFouIJKmkBK64Iiwo9+qr8L3vxa7oWxQEIiJJ+s1vQivg4YehVavY1eyQuoZERJLy0kvw29/C1VeHbSezlIJARCQJc+dC587QrBk8+WTG9hbYEwoCEZHytnJlGBw++GAYOTLsMZDFNEYgIlKetmwJ00Q/+wymToU6dcr+m8gUBCIi5alHD5gyJdwrcPLJsatJi7qGRETKS9++8MQTcPPN0KlT7GrSpiAQESkP06ZB9+7Qpg307h27mt2iIBAR2VsffwwXXAANGsCwYVChQuyKdouCQERkb2zYEDaY2bQJxoyBgw6KXdFu02CxiMiecoef/hTmzIFx48KCcjlIQSAisqd694YXXgiP55wTu5o9pq4hEZE9MW4c3HEHdOwIt9wSu5q9oiAQEdld8+fDZZfBCSdAv35ZvXxEOhQEIiK7Y/VqOO88qFwZRo+GKlViV7TXNEYgIpKu4mK49FJYtAgmT4Z69WJXVC4UBCIi6brtNnjtNXj6aWjePHY15UZdQyIi6Xj2WXjwwXD3cJcusaspVwoCEZGyzJwJXbvC6afDQw/FrqbcKQhERHZl6VI4//ywnPTw4VCxYuyKyl1iQWBm9cxsspnNN7P3zOyG1Os1zGyimX2Yejw4qRpERPbKxo0hBL74IiwfUbNm7IoSkWSLoBi4yd2PBpoBvzCzY4DbgEnu3hCYlPpdRCS7uMPPfw4zZoTxgeOPj11RYhILAndf6u7vpJ6vBeYDhwLtgEGptw0C2idVg4jIHnvkERg0CO65J6wsmscyMkZgZg2AE4AZQG13XwohLIBDdvI3Xc1slpnNWrFiRSbKFBEJJk6Em24K3UJ33x27msQlHgRmdgDwEtDD3b9M9+/cva+7F7l7Ua1atZIrUESktHnz4OKL4ZhjQpfQPvk/pybRT2hmFQkhMNTdR6ZeXmZmdVLH6wDLk6xBRCRt//kPnHlmWD5i7Fg44IDYFWVEkrOGDOgPzHf30hNvxwBXpp5fCbycVA0iImlbtiyEwMaN4e7hBg1iV5QxSS4x0Ry4AviHmc1JvfZroDcw3MyuAT4GOiRYg4hI2dasgbPOgiVL4PXX4bjjYleUUYkFgbtPA3a2NmurpM4rIrJbNmwIq4m+/37oDjrllNgVZZwWnRORwrVlC1xyCUybFjadP+us2BVFoSAQkcJUUhL2Gx43Dp58MgRCgcr/eVEiIttzh1/+EoYMgXvvDXcQFzAFgYgUnnvvhUcfhR494Ne/jl1NdAoCESksTzwR7hbu3DnsL5Dj+w2XBwWBiBSOYcPCxjLnnhs2nS+Au4bToX8KIlIYJkwIrYBTT4UXXsjLfQX2lIJARPLf9OlhBdHjjw/7ClSuHLuirKIgEJH89o9/wE9+AocdFloF1avHrijrKAhEJH8tXBjWD6paNSwtfcgOV70veLqhTETy02efwRlnwObNMHUqHH547IqyloJARPLPtkXkli2DSZPC3gKyUwoCEckvGzZA27Ywfz688go0bRq7oqynIBCR/LFlC3ToEGYJDR8euoakTAoCEckPJSVw1VUwfjz07QsXXRS7opyhWUMikvvc4YYb4Lnn4L774Gc/i11RTlEQiEju++1v4fHH4aab4NZbY1eTcxQEIpLbHnsMevaEq6+GPn20iNweUBCISO4aOhSuvx7atw/jAgqBPaIgEJHcNH58GBxu0SKsKrqv5r7sKQWBiOSeadPgwguhUSN4+WXYf//YFeU0BYGI5Ja5c8MNY/Xrw5//DAceGLuinKcgEJHc8e9/h6UjqlULi8jVqhW7orygTjURyQ1LloQ7hYuLYfLk0CKQcqEgEJHst3p1aAmsWAF/+QscfXTsivKKgkBEstv69WFjmX/+M8wUOvnk2BXlHQWBiGSvzZvDmkEzZsCLL0KrVrEryksKAhHJTlu3wpVXhu0l+/ULew5LIjRrSESyj3u4Y/j55+H+++Gaa2JXlNcUBCKSfe65B554Am65JfxIohILAjMbYGbLzWxeqdd6mtmnZjYn9XNOUucXkRz1yCPQq1doBfTuHbuagpBki2Ag0GYHr//R3RunfsYneH4RyTWDB0OPHmE84KmntIhchiQWBO7+BrAqqf99Eckz/fqFweGWLcOqolpELmNijBF0N7O/p7qODt7Zm8ysq5nNMrNZK1asyGR9IpJpDz4YdhVr0wbGjtUichmW6SB4Evge0BhYCjy4sze6e193L3L3olpaT0QkP7nDXXfBzTfDxRfD6NFQpUrsqgpORoPA3Ze5+1Z3LwGeBppk8vwikkVKSsI+w/feC126hP2GK1WKXVVBymgQmFmdUr+eD8zb2XtFJI8VF4etJR97LOwz3LcvVKgQu6qCldhojJkNA1oANc1sMXAP0MLMGgMOfAR0S+r8IpKlNm2CSy8N3UC9esEdd2h2UGSJBYG7d9zBy/2TOp+I5IB16+D88+H11+HRR+G662JXJGitIRHJlNWr4ZxzYOZMGDQIOneOXZGkKAhEJHmffRb2E1iwAEaMCK0CyRoKAhFJ1qJF0Lp12GHslVfCc8kqCgIRSc6CBWF7yXXrwrjAKafErkh2oMzpo2Z2XCYKEZE88+67cNppYXOZKVMUAlksnfsInjKzmWb2v2Z2UOIViUjumzYNWrSAypXD80aNYlcku1BmELj7j4DLgXrALDN7zszOSLwyEclNEybAmWdCnTohBBo2jF2RlCGtO4vd/UPgTuBW4MfAo2a2wMy0d5yIfG3ECDjvPDjySHjjDahXL3ZFkoZ0xgh+YGZ/BOYDLYFz3f3o1PM/JlyfiOSKAQPgkkugSROYPBkOOSR2RZKmdFoEjwPvAI3c/Rfu/g6Auy8htBJEpNA9/HDYUax1a3j1VThIw4m5pMzpo+5+2i6ODS7fckQkp7jDb34Tfi68MGwos99+sauS3VRmEJhZQ+A+4Bjgv7tFuPsRCdYlItmupARuvDHsMXz11WEFUe0qlpPS6Rp6hrChTDFwOvAsoJaASCErLg5dQY88EvYY7tdPIZDD0gmCyu4+CTB3X+TuPQkDxSJSiDZtCoPCAweGLqGHHoJ9Yux6K+UlnQjfaGb7AB+aWXfgU0DTAUQK0fr1YcG4iRPDAPENN8SuSMpBOjHeA6gCXA+cBHQCrkyyKBHJQmvWhBvFJk2CZ55RCOSRXbYIzKwCcLG7/wpYB1ydkapEJLssWxaWkX7/fXjxRbhA95Lmk10GgbtvNbOTzMzc3TNVlIhkkY8/DvcHfPopjBsXWgWSV9IZI3gXeNnMXgTWb3vR3UcmVpWIZIcPPgjLSH/5Jbz2GjRvHrsiSUA6QVAD+JxvzhRyQEEgks/mzPn62/+UKdC4cdRyJDnp3FmscQGRQjN9ethf+MADwwyhI4+MXZEkaLcm/5rZO0kVIiJZYuLE0B10yCFhGWmFQN7b3btALJEqRCQ7jBwJbduGPQSmToX69WNXJBmQzjLU3UvtTPZKwvWISCwDB0KHDnDSSWEZ6dq1Y1ckGZJOi+B/CDuTDQemmZlaBSL5xB369AkLx7VqFbqGDj44dlWSQelsVXkn0BDoD1xFWGri92b2vYRrE5GkrV8Pl18Ot9wSWgNjx0LVqrGrkgxLd6tKBz5L/RQDBwMjzOyBBGsTkSQtXAg//CE8/zz8/vfwwgvaS6BApbMfwfWEtYVWAv2AX7n7lm0L0QG3JFuiiJS7CRPgssvC8/HjoU2buPVIVOncUFYTuMDdF5V+0d1LzKxtMmWJSCLc4b774M474fjjYdQoOEJ7TBW6dG4ou3sXx+aXbzkikpi1a+HKK8PFv2NHePppjQcIkF6LQERy3QcfhH0E/vnPsJFMjx6gCYCSkti2QmY2wMyWm9m8Uq/VMLOJZvZh6lFz1ESSNmYMNGkCK1aEqaG//KVCQL4hyf3lBgLbj0DdBkxy94bApNTvIpKEkhK4+25o1w6+/32YPRtOPz12VZKFEgsCd38DWLXdy+2AQanng4D2SZ1fpKCtWQPnngu9eoUbxbRchOxCpscIarv7UgB3X2pmO9372My6Al0B6us/YJH0zZsXxgMWLYInnoCf/1xdQbJLSXYN7RV37+vuRe5eVKtWrdjliOSG4cOhWTNYty6sF3TttQoBKVOmg2CZmdUBSD0uz/D5RfJTcTHceitccgk0ahTGA7SbmKQp00EwhnCXMqnHlzN8fpH8s3IlnH02PPBAaAFMngx168auSnJIYmMEZjYMaAHUNLPFwD1Ab2C4mV0DfAx0SOr8IgXh3XfDeMDSpdC/P/z0p7ErkhyUWBC4e8edHGqV1DlFCsrgwdC1K9SsGXYSO/nk2BVJjsrawWIR2YktW+CGG6BzZ2jaNIwHKARkLygIRHLJsmXQujU8+mhYJmLixLC3sMhe0FpDIrlixgy48EJYtQqGDv16GWmRvaQWgUgu6NcPTjsNKlWC6dMVAlKuFAQi2WzTJujWDX72M2jRAmbNgsaNY1cleUZBIJKtPv00XPz79oXbbgs7idWoEbsqyUMaIxDJRlOnhs3k162DESPC2IBIQtQiEMkm7vD449CyJRx4IMycqRCQxCkIRLLFV1/BVVfBddeFJSPefhuOOSZ2VVIAFAQi2WDRIvjRj+DZZ6FnTxg9GqpXj12VFAiNEYjENmlSWDV0yxYYOxbato1dkRQYtQhEYtm8OXz7P/NMqF07TA1VCEgEahGIxDBzZlgp9L33oFOnsJNYtWqxq5ICpRaBSCZt2AA33QSnnAJffAHjxoVVRBUCEpFaBCKZMnkydOkCCxeGfYTvvz9MERWJTC0CkaR98UXYN6BlS9hnH5gyBZ58UiEgWUNBIJKkMWPCvQD9+8OvfgVz58KPfxy7KpFvUBCIJGH5crj0UmjXDr7znbCE9AMPQJUqsSsT+RYFgUh5cg97BRxzDIwaBb16hWmhRUWxKxPZKQ0Wi5SXTz6Ba6+FV16BZs1Cd5CWiJAcoBaByN4qKYGnnoJjjw0zgx5+OGwmrxCQHKEWgcje+PDDsGnMX/8KrVqFvQOOOCJ2VSK7RS0CkT1RXAx9+sAPfgBz5oRuoIkTFQKSk9QiENldc+fCNdfA7NnQvj386U9Qt27sqkT2mFoEIunatAnuuivMAPrkExg+HEaOVAhIzlOLQCQdb74ZWgHz50PnzvDQQ+H+AJE8oBaByK6sXw89ekDz5mH/4PHjYdAghYDkFbUIRHbm9dfDjKCPPoJf/ALuu0+rhEpeUotAZHurV4duoDPOgEqV4I03wobyCgHJUwoCkdJGjQo3gg0aBLfdFmYInXpq7KpEEhWla8jMPgLWAluBYnfXQiwS17JlcN118OKL0LhxWCbixBNjVyWSETHHCE5395URzy8SFokbPDgMCK9fD7/7XVguumLF2JWJZIwGi6VwffwxdOsGEybAD38Y7g4+6qjYVYlkXKwxAgdeM7PZZtZ1R28ws65mNsvMZq1YsSLD5UleW7MGevYMi8RNnQqPPhoeFQJSoGK1CJq7+xIzOwSYaGYL3P2N0m9w975AX4CioiKPUaTkmS+/hEceCTeDrVkD558fnjdoELsykaiitAjcfUnqcTkwCmgSow4pEGvXhr7/Bg3g7rvDVpHvvhuWh1AIiGQ+CMysqplV2/YcOBOYl+k6pACsWwe9e8N3vwt33hnuDp41C0aPDjODRASI0zVUGxhlZtvO/5y7T4hQh+Sr9evDiqB9+sDKlXDOOWFM4OSTY1cmkpUyHgTuvhBolOnzSgHYsAGefDJsEr98OZx1VgiAZs1iVyaS1XRnseS+r74K20MecQTcfHPYLOZvfwvTQhUCImXSfQSSuzZuhKefDovBLV0KLVvCiBHwox/FrkwkpygIJPds2gT9+oUA+PTTMAto2LDwKCK7TUEguWPzZhgwIEwFXbw4fPMfPBhOPz12ZSI5TWMEkv02b4a+faFhQ7j2WqhfP2wU/8YbCgGRcqAgkOy1ZUtY/+fII8OaQHXqhAHgadOgdWsIU5BFZC8pCCT7FBfDM8+EtX+6dIFatcIWkW++GaaEKgBEypXGCCR7FBfDc89Br17wr3+F/QDGjoWf/EQXf5EEqUUg8W3dCkOHhtVAr7wSDjgAXn45LAfRtq1CQCRhCgKJZ+vWMO3zuOOgUyfYf/+wENzs2XDeeQoAkQxREEjmlZTA8OHhDuDLLoMKFcIWke++G5aG3kf/WYpkksYIJHOWLg0tgP794f33wybxL7wAF12ki79IRAoCSda6dTBqFAwZAq+/HloDJ50UBoUvvji0BkQkKgWBlL/iYpg0Kdz1O2pUWBX08MPh9tvh8svh6KNjVygipSgIpHy4hz7+wYND98+yZXDQQWEQuFOnsCmMun9EspKCQPbOokWhm2fwYJg/HypWDFM+O3UKG8Lsv3/sCkWkDAoC2X1r1oTlngcPDuv9QFgA7qmnoEMHqFEjbn0islsUBJKezZvhz38OF/9x48JS0N//frgL+PLLw77AIpKTFASyc+5hfZ8hQ8I0z1Wrwro/3bqFrp+iIt30JZIHFATybR9+GC7+Q4bAwoVQuTK0bx8u/mecEcYBRCRvKAgkWLEifOsfMgRmzAjf9Fu1grvvhgsugGrVYlcoIglREBSyr76CMWPCxX/ChDD/v1Ej6NMHOnaEQw+NXaGIZICCoNCUlMCUKeHiP2IErF0bLvg33hi6fo4/PnaFIpJhCoJ85w6ffBK6e6ZPDxf/xYtDV89FF8EVV8Bpp2mpB5ECpiDIN+vWhXX833orXPxnzAiLvQHst18Y7H3wQTj33DAILCIFT0GQy0pKwt28M2Z8feGfNy+8DmGz91atoFkzaNo0LPtcqVLcmkUk6ygIcsny5d+86M+cGfr4AQ4+GJo0Cev5N20ann/nO3HrFZGcoCDIVhs3wpw5X1/033oLPvooHNt33/Dt/oorwkW/adPw7V+LuonIHlAQZAP3cONW6Yv+nDmwZUs4Xq9e6N7p3j1c9E88EapUiVuziOQNBUEMa9aEbp1tF/2ZM2HlynCsatWwdMONN379bb9u3bj1ikheUxAkZdMm+PzzsD7P55+HQd1t3/gXLAjvMQubtJx3XrjgN2sWtm/cV/9aRCRzolxxzKwN8AhQAejn7r1j1JGWLVvCxXzbBT3dxw0bvv2/VatWuNh36hQei4qgevXMfyYRkVIyHgRmVgH4E3AGsBh428zGuPv7iZ64uDh0yezOxXzVqq9n5ezIvvuGtfdr1AgzdOrXhxNO+Pr30o9HHAENGmi1ThHJOjFaBE2Af7n7QgAzex5oB5R/EPTqBQMHhgv6mjU7f98++4Tpl9su2nXqwLHHfvtivv0Fvlo1XdhFJOfFCIJDgU9K/b4YaLr9m8ysK9AVoH79+nt2pjp1QhfM9hf07R+rV9fUSxEpWDGCYEdfof1bL7j3BfoCFBUVfet4Wrp0CT8iIrJTMb4GLwbqlfr9MGBJhDpERIQ4QfA20NDMvmtmlYBLgTER6hARESJ0Dbl7sZl1B14lTB8d4O7vZboOEREJotxH4O7jgfExzi0iIt+kqTIiIgVOQSAiUuAUBCIiBU5BICJS4Mx9z+7VyiQzWwEs2sM/rwmsLMdycoE+c2HQZy4Me/OZD3f3WmW9KSeCYG+Y2Sx3L4pdRybpMxcGfebCkInPrK4hEZECpyAQESlwhRAEfWMXEIE+c2HQZy4MiX/mvB8jEBGRXSuEFoGIiOyCgkBEpMDldRCYWRsz+8DM/mVmt8WuJ2lmNsDMlpvZvNi1ZIKZ1TOzyWY238zeM7MbYteUNDPb38xmmtnc1Gf+TeyaMsXMKpjZu2Y2LnYtmWBmH5nZP8xsjpnNSvRc+TpGYGYVgH8CZxA2w3kb6Oju5b83cpYws9OAdcCz7n5c7HqSZmZ1gDru/o6ZVQNmA+3z/N+xAVXdfZ2ZVQSmATe4+1uRS0ucmd0IFAEHunvb2PUkzcw+AorcPfEb6PK5RdAE+Je7L3T3zcDzQLvINSXK3d8AVsWuI1Pcfam7v5N6vhaYT9gTO295sC71a8XUT35+myvFzA4DfgL0i11LPsrnIDgU+KTU74vJ84tEITOzBsAJwIy4lSQv1UUyB1gOTHT3vP/MwMPALUBJ7EIyyIHXzGy2mXVN8kT5HAS2g9fy/ptTITKzA4CXgB7u/mXsepLm7lvdvTFhv+8mZpbX3YBm1hZY7u6zY9eSYc3d/UTgbOAXqa7fRORzECwG6pX6/TBgSaRaJCGpfvKXgKHuPjJ2PZnk7muAKUCbyKUkrTlwXqrP/HmgpZkNiVtS8tx9SepxOTCK0N2diHwOgreBhmb2XTOrBFwKjIlck5Sj1MBpf2C+uz8Uu55MMLNaZnZQ6nlloDWwIG5VyXL32939MHdvQPj/8V/cvVPkshJlZlVTEyAws6rAmUBiswHzNgjcvRjoDrxKGEQc7u7vxa0qWWY2DHgTONLMFpvZNbFrSlhz4ArCN8Q5qZ9zYheVsDrAZDP7O+HLzkR3L4jplAWmNjDNzOYCM4FX3H1CUifL2+mjIiKSnrxtEYiISHoUBCIiBU5BICJS4BQEIiIFTkEgIlLgFAQiIgVOQSCyF8xs39g1iOwtBYFIKWZ2spn9PbXuf9XUmv/HbfeegWb2kJlNBu43syZmNj21Vv50Mzsy9b6pZta41N/9zcx+kOGPJFIm3VAmsh0zuxfYH6gMLHb3+7Y7PhCoCbRz961mdiCwwd2Lzaw1cK27X2hmVwInuHsPM/s+8Jy7F2X204iUTUEgsp3U2lRvAxuBH7r71u2ODwQmu/ug1O/1gEeBhoQVbiu6+1FmVgX4O3A00IsQKo9n7IOIpEldQyLfVgM4AKgG7G9mv9u2llGp96wv9bwXIRiOA84ltCZw9w3ARMKGSBcDz2WieJHdpYEukW/rC9wFfBe43927A3fs4v3VgU9Tz6/a7lg/YCww1d0LZvc4yS1qEYiUYmadgWJ3fw7oDZxsZi3L+LMHgPvM7G9AhdIHUpupfAk8k0S9IuVBYwQiCTKzuoTNY45y90LaZlFyiFrfs9dqAAAANUlEQVQEIglJtS5mAHcoBCSbqUUgIlLg1CIQESlwCgIRkQKnIBARKXAKAhGRAqcgEBEpcP8frJl27AFkaWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = linspace(0, 5, 10)\n",
    "y = x ** 2\n",
    "figure()\n",
    "plot(x, y, 'r')\n",
    "xlabel('x-ray')\n",
    "ylabel('y-ray')\n",
    "title('chart title')\n",
    "show()"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 1,
   "source": [
    "使用公式范例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$c = \\sqrt{a^2 + b^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from IPython.display import display\n",
    "\n",
    "from sympy.interactive import printing\n",
    "printing.init_printing(use_latex='mathjax')\n",
    "\n",
    "import sympy as sym\n",
    "from sympy import *\n",
    "x, y, z = symbols(\"x y z\")\n",
    "k, m, n = symbols(\"k m n\", integer=True)\n",
    "f, g, h = map(Function, 'fgh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
      ],
      "text/plain": [
       "        ⅈ⋅x \n3⋅π    ℯ    \n─── + ──────\n 2     2    \n      x  + y"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
